(1) Merge CentralTreeSumQuery and DistributedTreeSumQuery into one DPQuery to modularize things. The new query takes in an inner_query argument. Depending on the behavior of inner query, the query will follow central DP or distributed DP.

(2) Remove the hard-coded L1 clipping and replace with norm bound checking in the inner query. This design allows us to use whatever clipping factory we want outside the DPQuery.

PiperOrigin-RevId: 387398741

tensorflow_privacy/privacy/dp_query/tree_aggregation_query.py

@@ -15,13 +15,10 @@
 
 `TreeCumulativeSumQuery` and `TreeResidualSumQuery` are `DPQuery`s for continual
 online observation queries relying on `tree_aggregation`. 'Online' means that
-the leaf nodes of the tree arrive one by one as the time proceeds. The leaves
+the leaf nodes of the tree arrive one by one as the time proceeds.
-are vector records as defined in `dp_query.DPQuery`.
 
-`CentralTreeSumQuery` and `DistributedTreeSumQuery` are `DPQuery`s for
+`TreeRangeSumQuery` is a `DPQuery`s for offline tree aggregation protocol.
-central/distributed offline tree aggregation protocol. 'Offline' means all the
+'Offline' means all the leaf nodes are ready before the protocol starts.
-leaf nodes are ready before the protocol starts. Each record, different from
-what is defined in `dp_query.DPQuery`, is a histogram (i.e. the leaf nodes).
 """
 import distutils
 import math
@@ -29,7 +26,9 @@ from typing import Optional
 
 import attr
 import tensorflow as tf
+from tensorflow_privacy.privacy.dp_query import distributed_discrete_gaussian_query
 from tensorflow_privacy.privacy.dp_query import dp_query
+from tensorflow_privacy.privacy.dp_query import gaussian_query
 from tensorflow_privacy.privacy.dp_query import tree_aggregation
 
 
@@ -442,6 +441,171 @@ def _build_tree_from_leaf(leaf_nodes: tf.Tensor, arity: int) -> tf.RaggedTensor:
   return tree
 
 
+class TreeRangeSumQuery(dp_query.SumAggregationDPQuery):
+  """Implements dp_query for accurate range queries using tree aggregation.
+
+  Implements a variant of the tree aggregation protocol from. "Is interaction
+  necessary for distributed private learning?. Adam Smith, Abhradeep Thakurta,
+  Jalaj Upadhyay." Builds a tree on top of the input record and adds noise to
+  the tree for differential privacy. Any range query can be decomposed into the
+  sum of O(log(n)) nodes in the tree compared to O(n) when using a histogram.
+  Improves efficiency and reduces noise scale.
+  """
+
+  @attr.s(frozen=True)
+  class GlobalState(object):
+    """Class defining global state for TreeRangeSumQuery.
+
+    Attributes:
+      arity: The branching factor of the tree (i.e. the number of children each
+        internal node has).
+      inner_query_state: The global state of the inner query.
+    """
+    arity = attr.ib()
+    inner_query_state = attr.ib()
+
+  def __init__(self,
+               inner_query: dp_query.SumAggregationDPQuery,
+               arity: int = 2):
+    """Initializes the `TreeRangeSumQuery`.
+
+    Args:
+      inner_query: The inner `DPQuery` that adds noise to the tree.
+      arity: The branching factor of the tree (i.e. the number of children each
+        internal node has). Defaults to 2.
+    """
+    self._inner_query = inner_query
+    self._arity = arity
+
+    if self._arity < 1:
+      raise ValueError(f'Invalid arity={arity} smaller than 2.')
+
+  def initial_global_state(self):
+    """Implements `tensorflow_privacy.DPQuery.initial_global_state`."""
+    return TreeRangeSumQuery.GlobalState(
+        arity=self._arity,
+        inner_query_state=self._inner_query.initial_global_state())
+
+  def derive_sample_params(self, global_state):
+    """Implements `tensorflow_privacy.DPQuery.derive_sample_params`."""
+    return (global_state.arity,
+            self._inner_query.derive_sample_params(
+                global_state.inner_query_state))
+
+  def preprocess_record(self, params, record):
+    """Implements `tensorflow_privacy.DPQuery.preprocess_record`.
+
+    This method builds the tree, flattens it and applies
+    `inner_query.preprocess_record` to the flattened tree.
+
+    Args:
+      params: Hyper-parameters for preprocessing record.
+      record: A histogram representing the leaf nodes of the tree.
+
+    Returns:
+      A `tf.Tensor` representing the flattened version of the preprocessed tree.
+    """
+    arity, inner_query_params = params
+    preprocessed_record = _build_tree_from_leaf(record, arity).flat_values
+    preprocessed_record = self._inner_query.preprocess_record(
+        inner_query_params, preprocessed_record)
+
+    # The following codes reshape the output vector so the output shape of can
+    # be statically inferred. This is useful when used with
+    # `tff.aggregators.DifferentiallyPrivateFactory` because it needs to know
+    # the output shape of this function statically and explicitly.
+    preprocessed_record_shape = [
+        (self._arity**(math.ceil(math.log(record.shape[0], self._arity)) + 1) -
+         1) // (self._arity - 1)
+    ]
+    return tf.reshape(preprocessed_record, preprocessed_record_shape)
+
+  def get_noised_result(self, sample_state, global_state):
+    """Implements `tensorflow_privacy.DPQuery.get_noised_result`.
+
+    This function re-constructs the `tf.RaggedTensor` from the flattened tree
+    output by `preprocess_records.`
+
+    Args:
+      sample_state: A `tf.Tensor` for the flattened tree.
+      global_state: The global state of the protocol.
+
+    Returns:
+      A `tf.RaggedTensor` representing the tree.
+    """
+    # The [0] is needed because of how tf.RaggedTensor.from_two_splits works.
+    # print(tf.RaggedTensor.from_row_splits(values=[3, 1, 4, 1, 5, 9, 2, 6],
+    #                                       row_splits=[0, 4, 4, 7, 8, 8]))
+    # <tf.RaggedTensor [[3, 1, 4, 1], [], [5, 9, 2], [6], []]>
+    # This part is not written in tensorflow and will be executed on the server
+    # side instead of the client side if used with
+    # tff.aggregators.DifferentiallyPrivateFactory for federated learning.
+    row_splits = [0] + [
+        (self._arity**(x + 1) - 1) // (self._arity - 1) for x in range(
+            math.floor(math.log(sample_state.shape[0], self._arity)) + 1)
+    ]
+    tree = tf.RaggedTensor.from_row_splits(
+        values=sample_state, row_splits=row_splits)
+    return tree, global_state
+
+  @classmethod
+  def build_central_gaussian_query(cls,
+                                   l2_norm_clip: float,
+                                   stddev: float,
+                                   arity: int = 2):
+    """Returns `TreeRangeSumQuery` with central Gaussian noise.
+
+    Args:
+      l2_norm_clip: Each record should be clipped so that it has L2 norm at most
+        `l2_norm_clip`.
+      stddev: Stddev of the central Gaussian noise.
+      arity: The branching factor of the tree (i.e. the number of children each
+        internal node has). Defaults to 2.
+    """
+    if l2_norm_clip <= 0:
+      raise ValueError(f'`l2_norm_clip` must be positive, got {l2_norm_clip}.')
+
+    if stddev < 0:
+      raise ValueError(f'`stddev` must be non-negative, got {stddev}.')
+
+    if arity < 2:
+      raise ValueError(f'`arity` must be at least 2, got {arity}.')
+
+    inner_query = gaussian_query.GaussianSumQuery(l2_norm_clip, stddev)
+
+    return cls(arity=arity, inner_query=inner_query)
+
+  @classmethod
+  def build_distributed_discrete_gaussian_query(cls,
+                                                l2_norm_bound: float,
+                                                local_stddev: float,
+                                                arity: int = 2):
+    """Returns `TreeRangeSumQuery` with central Gaussian noise.
+
+    Args:
+      l2_norm_bound: Each record should be clipped so that it has L2 norm at
+        most `l2_norm_bound`.
+      local_stddev: Scale/stddev of the local discrete Gaussian noise.
+      arity: The branching factor of the tree (i.e. the number of children each
+        internal node has). Defaults to 2.
+    """
+    if l2_norm_bound <= 0:
+      raise ValueError(
+          f'`l2_clip_bound` must be positive, got {l2_norm_bound}.')
+
+    if local_stddev < 0:
+      raise ValueError(
+          f'`local_stddev` must be non-negative, got {local_stddev}.')
+
+    if arity < 2:
+      raise ValueError(f'`arity` must be at least 2, got {arity}.')
+
+    inner_query = distributed_discrete_gaussian_query.DistributedDiscreteGaussianSumQuery(
+        l2_norm_bound, local_stddev)
+
+    return cls(arity=arity, inner_query=inner_query)
+
+
 def _get_add_noise(stddev, seed: int = None):
   """Utility function to decide which `add_noise` to use according to tf version."""
   if distutils.version.LooseVersion(

tensorflow_privacy/privacy/dp_query/tree_aggregation_query_test.py

@@ -423,6 +423,137 @@ class BuildTreeTest(tf.test.TestCase, parameterized.TestCase):
         self.assertEqual(tree[layer][idx], expected_value)
 
 
+class TreeRangeSumQueryTest(tf.test.TestCase, parameterized.TestCase):
+
+  @parameterized.product(
+      inner_query=['central', 'distributed'],
+      params=[(0., 1., 2), (1., -1., 2), (1., 1., 1)],
+  )
+  def test_raises_error(self, inner_query, params):
+    clip_norm, stddev, arity = params
+    with self.assertRaises(ValueError):
+      if inner_query == 'central':
+        tree_aggregation_query.TreeRangeSumQuery.build_central_gaussian_query(
+            clip_norm, stddev, arity)
+      elif inner_query == 'distributed':
+        tree_aggregation_query.TreeRangeSumQuery.build_distributed_discrete_gaussian_query(
+            clip_norm, stddev, arity)
+
+  @parameterized.product(
+      inner_query=['central', 'distributed'],
+      clip_norm=[0.1, 1.0, 10.0],
+      stddev=[0.1, 1.0, 10.0])
+  def test_initial_global_state_type(self, inner_query, clip_norm, stddev):
+
+    if inner_query == 'central':
+      query = tree_aggregation_query.TreeRangeSumQuery.build_central_gaussian_query(
+          clip_norm, stddev)
+    elif inner_query == 'distributed':
+      query = tree_aggregation_query.TreeRangeSumQuery.build_distributed_discrete_gaussian_query(
+          clip_norm, stddev)
+    global_state = query.initial_global_state()
+    self.assertIsInstance(global_state,
+                          tree_aggregation_query.TreeRangeSumQuery.GlobalState)
+
+  @parameterized.product(
+      inner_query=['central', 'distributed'],
+      clip_norm=[0.1, 1.0, 10.0],
+      stddev=[0.1, 1.0, 10.0],
+      arity=[2, 3, 4])
+  def test_derive_sample_params(self, inner_query, clip_norm, stddev, arity):
+    if inner_query == 'central':
+      query = tree_aggregation_query.TreeRangeSumQuery.build_central_gaussian_query(
+          clip_norm, stddev, arity)
+    elif inner_query == 'distributed':
+      query = tree_aggregation_query.TreeRangeSumQuery.build_distributed_discrete_gaussian_query(
+          clip_norm, stddev, arity)
+    global_state = query.initial_global_state()
+    derived_arity, inner_query_state = query.derive_sample_params(global_state)
+    self.assertAllClose(derived_arity, arity)
+    if inner_query == 'central':
+      self.assertAllClose(inner_query_state, clip_norm)
+    elif inner_query == 'distributed':
+      self.assertAllClose(inner_query_state.l2_norm_bound, clip_norm)
+      self.assertAllClose(inner_query_state.local_stddev, stddev)
+
+  @parameterized.product(
+      (dict(arity=2, expected_tree=[1, 1, 0, 1, 0, 0, 0]),
+       dict(arity=3, expected_tree=[1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0])),
+      inner_query=['central', 'distributed'],
+  )
+  def test_preprocess_record(self, inner_query, arity, expected_tree):
+    if inner_query == 'central':
+      query = tree_aggregation_query.TreeRangeSumQuery.build_central_gaussian_query(
+          10., 0., arity)
+      record = tf.constant([1, 0, 0, 0], dtype=tf.float32)
+      expected_tree = tf.cast(expected_tree, tf.float32)
+    elif inner_query == 'distributed':
+      query = tree_aggregation_query.TreeRangeSumQuery.build_distributed_discrete_gaussian_query(
+          10., 0., arity)
+      record = tf.constant([1, 0, 0, 0], dtype=tf.int32)
+    global_state = query.initial_global_state()
+    params = query.derive_sample_params(global_state)
+    preprocessed_record = query.preprocess_record(params, record)
+    self.assertAllClose(preprocessed_record, expected_tree)
+
+  @parameterized.named_parameters(
+      ('stddev_1', 1, tf.constant([1, 0], dtype=tf.int32), [1, 1, 0]),
+      ('stddev_0_1', 4, tf.constant([1, 0], dtype=tf.int32), [1, 1, 0]),
+  )
+  def test_distributed_preprocess_record_with_noise(self, local_stddev, record,
+                                                    expected_tree):
+    query = tree_aggregation_query.TreeRangeSumQuery.build_distributed_discrete_gaussian_query(
+        10., local_stddev)
+    global_state = query.initial_global_state()
+    params = query.derive_sample_params(global_state)
+
+    preprocessed_record = query.preprocess_record(params, record)
+
+    self.assertAllClose(
+        preprocessed_record, expected_tree, atol=10 * local_stddev)
+
+  @parameterized.product(
+      (dict(
+          arity=2,
+          expected_tree=tf.ragged.constant([[1], [1, 0], [1, 0, 0, 0]])),
+       dict(
+           arity=3,
+           expected_tree=tf.ragged.constant([[1], [1, 0, 0],
+                                             [1, 0, 0, 0, 0, 0, 0, 0, 0]]))),
+      inner_query=['central', 'distributed'],
+  )
+  def test_get_noised_result(self, inner_query, arity, expected_tree):
+    if inner_query == 'central':
+      query = tree_aggregation_query.TreeRangeSumQuery.build_central_gaussian_query(
+          10., 0., arity)
+      record = tf.constant([1, 0, 0, 0], dtype=tf.float32)
+      expected_tree = tf.cast(expected_tree, tf.float32)
+    elif inner_query == 'distributed':
+      query = tree_aggregation_query.TreeRangeSumQuery.build_distributed_discrete_gaussian_query(
+          10., 0., arity)
+      record = tf.constant([1, 0, 0, 0], dtype=tf.int32)
+    global_state = query.initial_global_state()
+    params = query.derive_sample_params(global_state)
+    preprocessed_record = query.preprocess_record(params, record)
+    sample_state, global_state = query.get_noised_result(
+        preprocessed_record, global_state)
+
+    self.assertAllClose(sample_state, expected_tree)
+
+  @parameterized.product(stddev=[0.1, 1.0, 10.0])
+  def test_central_get_noised_result_with_noise(self, stddev):
+    query = tree_aggregation_query.TreeRangeSumQuery.build_central_gaussian_query(
+        10., stddev)
+    global_state = query.initial_global_state()
+    params = query.derive_sample_params(global_state)
+    preprocessed_record = query.preprocess_record(params, tf.constant([1., 0.]))
+    sample_state, global_state = query.get_noised_result(
+        preprocessed_record, global_state)
+
+    self.assertAllClose(
+        sample_state, tf.ragged.constant([[1.], [1., 0.]]), atol=10 * stddev)
+
+
 class CentralTreeSumQueryTest(tf.test.TestCase, parameterized.TestCase):
 
   def test_initial_global_state_type(self):